New AI network segments eye anomalies for TCM diagnostics

By PulseAugur Editorial · [1 sources] · 2026-06-04 04:00

Researchers have developed HD-DinoMoE, a novel network designed for segmenting scleral anomalies in ocular inspection images. This system aims to bring objectivity and quantification to Traditional Chinese Medicine's ocular diagnostics. The network employs a class-aware hierarchical dual mixture-of-experts approach to handle variations in image acquisition, anomaly types, and specular reflections, achieving competitive segmentation results on a new benchmark dataset. AI

IMPACT This model could enable more objective and scalable ocular diagnostics in Traditional Chinese Medicine.

RANK_REASON This is a research paper describing a novel AI model for a specific segmentation task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

arXiv cs.CV TIER_1 English(EN) · Yinxiang Yu, Maoxiang Chu, Qi Niu, Guanghu Liu, Wei Xu, Haotian Wang, Zhi Chen, Yutian Zhu, Yuelong Fan, Guanghao Liao · 2026-06-04 04:00

HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios

arXiv:2606.04888v1 Announce Type: new Abstract: Traditional Chinese Medicine (TCM) ocular inspection provides empirical cues for assessing scleral surface anomalies, but its clinical use remains subjective and difficult to quantify. To support intelligent and quantifiable ocular …

COVERAGE [1]

HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios

RELATED ENTITIES

RELATED TOPICS